Speech-based detection of multi-class Alzheimer’s disease classification using machine learning

Abstract

Alzheimer's disease is a severe neurological condition that affects numerous people globally with detrimental consequences. Detecting AD early is crucial for prompt treatment and effective management. This study presents a novel approach for classifying six different types of cognitive impairment using speech-based analysis, including probable AD, possible AD, MCI, memory impairments, vascular dementia, and control. The method employs speech data from DementiaBank's Pitt Corpus, which is preprocessed to extract pertinent acoustic features. The characteristics are subsequently employed to educate ve machine learning algorithms, namely KNN, DT, SVM, XGBoost, and RF. The effectiveness of every algorithm is assessed through a 10-fold cross-validation. According to the research ndings, the suggested method that is based on speech obtains a total accuracy of 75.59% concerning the six-class categorization issue The proposed approach can be developed into a non-invasive, cost-effective, and accessible diagnostic tool for the early detection and management of cognitive impairment.